1
|
Lootens S, Janssens I, Van Den Abeele R, Wülfers EM, Bezerra AS, Verstraeten B, Hendrickx S, Okenov A, Nezlobinsky T, Panfilov AV, Vandersickel N. Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise. Comput Biol Med 2024; 171:108138. [PMID: 38401451 DOI: 10.1016/j.compbiomed.2024.108138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F2-scores remain high, yielding F2≥0.931 as opposed to the best PM F2≥0.635 across all 64 simulations.
Collapse
Affiliation(s)
| | - Iris Janssens
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | | | - Eike M Wülfers
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | | | - Bjorn Verstraeten
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Sander Hendrickx
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Arstanbek Okenov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Timur Nezlobinsky
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Alexander V Panfilov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium; World-Class Research Center "Digital Biodesign and personalised healthcare", Sechenov University, Moscow 119991, Russia; Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg 620002, Russia
| | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| |
Collapse
|
2
|
Shi X, Sau A, Li X, Patel K, Bajaj N, Varela M, Wu H, Handa B, Arnold A, Shun-Shin M, Keene D, Howard J, Whinnett Z, Peters N, Christensen K, Jensen HJ, Ng FS. Information theory-based direct causality measure to assess cardiac fibrillation dynamics. J R Soc Interface 2023; 20:20230443. [PMID: 37817583 PMCID: PMC10565370 DOI: 10.1098/rsif.2023.0443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/19/2023] [Indexed: 10/12/2023] Open
Abstract
Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability may fail if the assumption is not met or given sparsely sampled, high-dimensional data. More recently developed information theory-based causality measures could potentially provide a more accurate estimate of the nonlinear coupling. However, despite their successful application to linear and nonlinear physical systems, their use is not known in the clinical field. Partial mutual information from mixed embedding (PMIME) was implemented to identify the direct coupling of cardiac electrophysiology signals. We show that PMIME requires less data and is more robust to extrinsic confounding factors. The algorithms were then extended for efficient characterization of fibrillation organization and hierarchy using clinical high-dimensional data. We show that PMIME network measures correlate well with the spatio-temporal organization of fibrillation and demonstrated that hierarchical type of fibrillation and drivers could be identified in a subset of ventricular fibrillation patients, such that regions of high hierarchy are associated with high dominant frequency.
Collapse
Affiliation(s)
- Xili Shi
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Arunashis Sau
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Xinyang Li
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Kiran Patel
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Nikesh Bajaj
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Marta Varela
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Huiyi Wu
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Balvinder Handa
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Ahran Arnold
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Matthew Shun-Shin
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Daniel Keene
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - James Howard
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Zachary Whinnett
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Nicholas Peters
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Kim Christensen
- Department of Physics, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
| | - Henrik Jeldtoft Jensen
- Department of Mathematics, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
- Department of Cardiology, Chelsea and Westminster NHS Foundation Trust, London, UK
| |
Collapse
|
3
|
Van Den Abeele R, Hendrickx S, Van Nieuwenhuyse E, Dunnink A, Panfilov AV, Vos MA, Wülfers EM, Vandersickel N. Directed graph mapping shows rotors maintain non-terminating and focal sources maintain self-terminating Torsade de Pointes in canine model. Front Physiol 2023; 14:1201260. [PMID: 37565147 PMCID: PMC10411729 DOI: 10.3389/fphys.2023.1201260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 06/28/2023] [Indexed: 08/12/2023] Open
Abstract
Torsade de Pointes is a polymorphic ventricular tachycardia which is as yet incompletely understood. While the onset of a TdP episode is generally accepted to be caused by triggered activity, the mechanisms for the perpetuation is still under debate. In this study, we analysed data from 54 TdP episodes divided over 5 dogs (4 female, 1 male) with chronic atrioventricular block. Previous research on this dataset showed both reentry and triggered activity to perpetuate the arrhythmia. 13 of those TdP episodes showed reentry as part of the driving mechanism of perpetuating the episode. The remaining 41 episodes were purely ectopic. Reentry was the main mechanism in long-lasting episodes (>14 beats), while focal sources were responsible for maintaining shorter episodes. Building on these results, we re-analysed the data using directed graph mapping This program uses principles from network theory and a combination of positional data and local activation times to identify reentry loops and focal sources within the data. The results of this study are twofold. First, concerning reentry loops, we found that on average non-terminating (NT) episodes (≥10 s) show significantly more simultaneous reentry loops than self-terminating (ST) TdP (<10 s). Non-terminating episodes have on average 2.72 ± 1.48 simultaneous loops, compared to an average of 1.33 ± 0.66 for self-terminating episodes. In addition, each NT episode showed a presence of (bi-)ventricular loops between 10.10% and 69.62% of their total reentry duration. Compared to the ST episodes, only 1 in 4 episodes (25%) showed (bi-)ventricular reentry, lasting only 7.12% of its total reentry duration. This suggests that while focal beats trigger TdP, macro-reentry and multiple simultaneous localized reentries are the major drivers of long-lasting episodes. Second, using heatmaps, we found focal sources to occur in preferred locations, instead of being distributed randomly. This may have implications on treatment if such focal origins can be disabled reliably.
Collapse
Affiliation(s)
- Robin Van Den Abeele
- Biophysics Group, Department of Physics and Astronomy, Faculty of Sciences, Ghent University, Ghent, Belgium
| | - Sander Hendrickx
- Biophysics Group, Department of Physics and Astronomy, Faculty of Sciences, Ghent University, Ghent, Belgium
| | - Enid Van Nieuwenhuyse
- Biophysics Group, Department of Physics and Astronomy, Faculty of Sciences, Ghent University, Ghent, Belgium
| | - Albert Dunnink
- Department of Medical Physiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Alexander V. Panfilov
- Biophysics Group, Department of Physics and Astronomy, Faculty of Sciences, Ghent University, Ghent, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Yekaterinburg, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov University, Moscow, Russia
| | - Marc A. Vos
- Department of Medical Physiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Eike M. Wülfers
- Biophysics Group, Department of Physics and Astronomy, Faculty of Sciences, Ghent University, Ghent, Belgium
| | - Nele Vandersickel
- Biophysics Group, Department of Physics and Astronomy, Faculty of Sciences, Ghent University, Ghent, Belgium
| |
Collapse
|
4
|
Özgül O, Hermans BJ, van Hunnik A, Verheule S, Schotten U, Bonizzi P, Zeemering S. High-density and high coverage composite mapping of repetitive atrial activation patterns. Comput Biol Med 2023; 159:106920. [PMID: 37119551 DOI: 10.1016/j.compbiomed.2023.106920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/18/2023] [Accepted: 04/13/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND Repetitive atrial activation patterns (RAAPs) during atrial fibrillation (AF) may be associated with localized mechanisms that maintain AF. Current electro-anatomical mapping systems are unsuitable for analyzing RAAPs due to the trade-off between spatial coverage and electrode density in clinical catheters. This work proposes a technique to overcome this trade-off by constructing composite maps from spatially overlapping sequential recordings. METHODS High-density epicardial contact mapping was performed during open-chest surgery in goats (n=16, left and right atria) with 3 or 22 weeks of sustained AF (249-electrode array, electrode distance 2.4 mm). A dataset mimicking sequential recordings was generated by segmenting the grid into four spatially overlapping regions (each region 6.5 cm2, 48±10% overlap) without temporal overlap. RAAPs were detected in each region using recurrence plots of activation times. RAAPs in two different regions were joined in case of RAAP cross-recurrence between overlapping electrodes. We quantified the reconstruction success rate and quality of the composite maps. RESULTS Of 1021 RAAPs found in the full mapping array (32±13 per recording), 328 spatiotemporally stable RAAPs were analyzed. 247 composite maps were generated (75% success) with a quality of 0.86±0.21 (Pearson correlation). Success was significantly affected by the RAAP area. Quality was weakly correlated with the number of repetitions of RAAPs (r=0.13, p<0.05) and not affected by the atrial side (left or right) or AF duration (3 or 22 weeks of AF). CONCLUSIONS Constructing composite maps by combining spatially overlapping sequential recordings is feasible. Interpretation of these maps can play a central role in ablation planning.
Collapse
Affiliation(s)
- Ozan Özgül
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Ben Jm Hermans
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Arne van Hunnik
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Sander Verheule
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Ulrich Schotten
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands.
| | - Pietro Bonizzi
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, the Netherlands
| | - Stef Zeemering
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| |
Collapse
|
5
|
Vila M, Rivolta MW, Barrios Espinosa CA, Unger LA, Luik A, Loewe A, Sassi R. Recommender system for ablation lines to treat complex atrial tachycardia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107406. [PMID: 36787660 DOI: 10.1016/j.cmpb.2023.107406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 01/30/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT. METHODS The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine. RESULTS The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time. CONCLUSIONS The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT.
Collapse
Affiliation(s)
- Muhamed Vila
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy
| | - Massimo W Rivolta
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy.
| | - Cristian A Barrios Espinosa
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Laura A Unger
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Moltkestraße 90, Karlsruhe, 76133, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Roberto Sassi
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy
| |
Collapse
|
6
|
Corrado C, Roney CH, Razeghi O, Lemus JAS, Coveney S, Sim I, Williams SE, O'Neill MD, Wilkinson RD, Clayton RH, Niederer SA. Quantifying the impact of shape uncertainty on predicted arrhythmias. Comput Biol Med 2023; 153:106528. [PMID: 36634600 DOI: 10.1016/j.compbiomed.2022.106528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/15/2022] [Accepted: 12/31/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Personalised computer models are increasingly used to diagnose cardiac arrhythmias and tailor treatment. Patient-specific models of the left atrium are often derived from pre-procedural imaging of anatomy and fibrosis. These images contain noise that can affect simulation predictions. There are few computationally tractable methods for propagating uncertainties from images to clinical predictions. METHOD We describe the left atrium anatomy using our Bayesian shape model that captures anatomical uncertainty in medical images and has been validated on 63 independent clinical images. This algorithm describes the left atrium anatomy using Nmodes=15 principal components, capturing 95% of the shape variance and calculated from 70 clinical cardiac magnetic resonance (CMR) images. Latent variables encode shape uncertainty: we evaluate their posterior distribution for each new anatomy. We assume a normally distributed prior. We use the unscented transform to sample from the posterior shape distribution. For each sample, we assign the local material properties of the tissue using the projection of late gadolinium enhancement CMR (LGE-CMR) onto the anatomy to estimate local fibrosis. To test which activation patterns an atrium can sustain, we perform an arrhythmia simulation for each sample. We consider 34 possible outcomes (31 macro-re-entries, functional re-entry, atrial fibrillation, and non-sustained arrhythmia). For each sample, we determine the outcome by comparing pre- and post-ablation activation patterns following a cross-field stimulus. RESULTS We create patient-specific atrial electrophysiology models of ten patients. We validate the mean and standard deviation maps from the unscented transform with the same statistics obtained with 12,000 Monte Carlo (ground truth) samples. We found discrepancies <3% and <2% for the mean and standard deviation for fibrosis burden and activation time, respectively. For each patient case, we then compare the predicted outcome from a model built on the clinical data (deterministic approach) with the probability distribution obtained from the simulated samples. We found that the deterministic approach did not predict the most likely outcome in 80% of the cases. Finally, we estimate the influence of each source of uncertainty independently. Fixing the anatomy to the posterior mean and maintaining uncertainty in fibrosis reduced the prediction of self-terminating arrhythmias from ≃14% to ≃7%. Keeping the fibrosis fixed to the sample mean while retaining uncertainty in shape decreased the prediction of substrate-driven arrhythmias from ≃33% to ≃18% and increased the prediction of macro-re-entries from ≃54% to ≃68%. CONCLUSIONS We presented a novel method for propagating shape uncertainty in atrial models through to uncertainty in numerical simulations. The algorithm takes advantage of the unscented transform to compute the output distribution of the outcomes. We validated the unscented transform as a viable sampling strategy to deal with anatomy uncertainty. We then showed that the prediction computed with a deterministic model does not always coincide with the most likely outcome. Finally, we found that shape uncertainty affects the predictions of macro-re-entries, while fibrosis uncertainty affects the predictions of functional re-entries.
Collapse
Affiliation(s)
- Cesare Corrado
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom.
| | - Caroline H Roney
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom; School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom
| | - Orod Razeghi
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom; UCL Centre for Advanced Research Computing, London, United Kingdom
| | - Josè Alonso Solís Lemus
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Iain Sim
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Steven E Williams
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Mark D O'Neill
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Richard D Wilkinson
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven A Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| |
Collapse
|
7
|
Fambuena-Santos C, Hernández-Romero I, Molero R, Atienza F, Climent AM, Guillem MS. AF driver detection in pulmonary vein area by electropcardiographic imaging: Relation with a favorable outcome of pulmonary vein isolation. Front Physiol 2023; 14:1057700. [PMID: 36793415 PMCID: PMC9922892 DOI: 10.3389/fphys.2023.1057700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/10/2023] [Indexed: 01/31/2023] Open
Abstract
Pulmonary vein isolation (PVI) is the most successful treatment for atrial fibrillation (AF) nowadays. However, not all AF patients benefit from PVI. In this study, we evaluate the use of ECGI to identify reentries and relate rotor density in the pulmonary vein (PV) area as an indicator of PVI outcome. Rotor maps were computed in a set of 29 AF patients using a new rotor detection algorithm. The relationship between the distribution of reentrant activity and the clinical outcome after PVI was studied. The number of rotors and proportion of PSs in different atrial regions were computed and compared retrospectively in two groups of patients: patients that remained in sinus rhythm 6 months after PVI and patients with arrhythmia recurrence. The total number of rotors obtained was higher in patients returning to arrhythmia after the ablation (4.31 ± 2.77 vs. 3.58 ± 2.67%, p = 0.018). However, a significantly higher concentration of PSs in the pulmonary veins was found in patients that remained in sinus rhythm (10.20 ± 12.40% vs. 5.19 ± 9.13%, p = 0.011) 6 months after PVI. The results obtained show a direct relationship between the expected AF mechanism and the electrophysiological parameters provided by ECGI, suggesting that this technology offers relevant information to predict the clinical outcome after PVI in AF patients.
Collapse
Affiliation(s)
- Carlos Fambuena-Santos
- COR Laboratory, ITACA Institute, Universitat Politècnica de València, Valencia, Spain,*Correspondence: Carlos Fambuena-Santos,
| | | | - Rubén Molero
- COR Laboratory, ITACA Institute, Universitat Politècnica de València, Valencia, Spain
| | - Felipe Atienza
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Madrid, Spain,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Andreu M. Climent
- COR Laboratory, ITACA Institute, Universitat Politècnica de València, Valencia, Spain
| | - M S. Guillem
- COR Laboratory, ITACA Institute, Universitat Politècnica de València, Valencia, Spain
| |
Collapse
|
8
|
Ripplinger CM, Glukhov AV, Kay MW, Boukens BJ, Chiamvimonvat N, Delisle BP, Fabritz L, Hund TJ, Knollmann BC, Li N, Murray KT, Poelzing S, Quinn TA, Remme CA, Rentschler SL, Rose RA, Posnack NG. Guidelines for assessment of cardiac electrophysiology and arrhythmias in small animals. Am J Physiol Heart Circ Physiol 2022; 323:H1137-H1166. [PMID: 36269644 PMCID: PMC9678409 DOI: 10.1152/ajpheart.00439.2022] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/11/2022] [Accepted: 10/17/2022] [Indexed: 01/09/2023]
Abstract
Cardiac arrhythmias are a major cause of morbidity and mortality worldwide. Although recent advances in cell-based models, including human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM), are contributing to our understanding of electrophysiology and arrhythmia mechanisms, preclinical animal studies of cardiovascular disease remain a mainstay. Over the past several decades, animal models of cardiovascular disease have advanced our understanding of pathological remodeling, arrhythmia mechanisms, and drug effects and have led to major improvements in pacing and defibrillation therapies. There exist a variety of methodological approaches for the assessment of cardiac electrophysiology and a plethora of parameters may be assessed with each approach. This guidelines article will provide an overview of the strengths and limitations of several common techniques used to assess electrophysiology and arrhythmia mechanisms at the whole animal, whole heart, and tissue level with a focus on small animal models. We also define key electrophysiological parameters that should be assessed, along with their physiological underpinnings, and the best methods with which to assess these parameters.
Collapse
Affiliation(s)
- Crystal M Ripplinger
- Department of Pharmacology, University of California Davis School of Medicine, Davis, California
| | - Alexey V Glukhov
- Department of Medicine, Cardiovascular Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Matthew W Kay
- Department of Biomedical Engineering, The George Washington University, Washington, District of Columbia
| | - Bastiaan J Boukens
- Department Physiology, University Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Medical Biology, University of Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Nipavan Chiamvimonvat
- Department of Pharmacology, University of California Davis School of Medicine, Davis, California
- Department of Internal Medicine, University of California Davis School of Medicine, Davis, California
- Veterans Affairs Northern California Healthcare System, Mather, California
| | - Brian P Delisle
- Department of Physiology, University of Kentucky, Lexington, Kentucky
| | - Larissa Fabritz
- University Center of Cardiovascular Science, University Heart and Vascular Center, University Hospital Hamburg-Eppendorf with DZHK Hamburg/Kiel/Luebeck, Germany
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Thomas J Hund
- Department of Internal Medicine, Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio
- Department of Biomedical Engineering, Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio
| | - Bjorn C Knollmann
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Na Li
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Katherine T Murray
- Departments of Medicine and Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Steven Poelzing
- Virginia Tech Carilon School of Medicine, Center for Heart and Reparative Medicine Research, Fralin Biomedical Research Institute at Virginia Tech, Roanoke, Virginia
- Department of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia
| | - T Alexander Quinn
- Department of Physiology and Biophysics, Dalhousie University, Halifax, Nova Scotia, Canada
- School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Carol Ann Remme
- Department of Experimental Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
| | - Stacey L Rentschler
- Cardiovascular Division, Department of Medicine, Washington University in Saint Louis, School of Medicine, Saint Louis, Missouri
| | - Robert A Rose
- Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Physiology and Pharmacology, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Nikki G Posnack
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, District of Columbia
- Department of Pediatrics, George Washington University School of Medicine, Washington, District of Columbia
| |
Collapse
|
9
|
Hawson J, Van Nieuwenhuyse E, Van Den Abeele R, Al-Kaisey A, Anderson RD, Chieng D, Segan L, Watts T, Campbell T, Hendrickx S, Morton J, McLellan A, Kistler P, Lee A, Gerstenfeld EP, Hsia HH, Voskoboinik A, Pathik B, Kumar S, Kalman J, Lee G, Vandersickel N. Directed Graph Mapping for Ventricular Tachycardia: A Comparison to Established Mapping Techniques. JACC Clin Electrophysiol 2022:S2405-500X(22)00723-X. [PMID: 36752465 DOI: 10.1016/j.jacep.2022.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/04/2022] [Accepted: 08/15/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Understanding underlying mechanism(s) and identifying critical circuit components are fundamental to successful ventricular tachycardia (VT) ablation. Directed graph mapping (DGM) offers a novel technique to identify the mechanism and critical components of a VT circuit. OBJECTIVES This study sought to evaluate the accuracy of DGM in VT ablation compared with traditional mapping techniques and a commercially available automated conduction velocity mapping (ACVM) tool. METHODS Patients with structural heart disease who had undergone a VT ablation with entrainment-proven critical isthmus and a high-density electroanatomical activation map were included. Traditional mapping (TM) consisted of a combination of local activation time and entrainment mapping and was considered the gold standard for determining the VT mechanism, circuit, and isthmus location. The same local activation time values were then processed using DGM and a commercially available ACVM (Coherent Mapping, Biosense Webster) tool. The aim of this study was to compare TM vs DGM and ACVM in their ability to identify the VT mechanism, characterize the VT circuit, and locate the critical isthmus. RESULTS Thirty-five cases were identified. TM classified the VT mechanism as focal in 7 patients and re-entrant in 28 patients. TM classified 11 VTs as single-loop re-entry, 15 as dual-loop re-entry, 1 as complex, and 1 case was indeterminant. The overall agreement between DGM and TM for determining VT mechanism and circuit type was strong (kappa value = 0.79; P < 0.01), as was the agreement between ACVM and TM (kappa value = 0.66; P < 0.01). Both DGM and ACVM identified the putative VT isthmus in 25 (89%) of the re-entrant cases. Focal activation was correctly identified by both techniques in all cases. CONCLUSIONS DGM is a rapid automated algorithm that has a strong level of agreement with TM for manually re-annotated VT maps.
Collapse
Affiliation(s)
- Joshua Hawson
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia; Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, Australia
| | | | | | - Ahmed Al-Kaisey
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia; Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert D Anderson
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia; Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, Australia
| | - David Chieng
- Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, Australia; Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Louise Segan
- Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, Australia; Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Troy Watts
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Timothy Campbell
- Western Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Sander Hendrickx
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Joseph Morton
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Alexander McLellan
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Peter Kistler
- Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, Australia; Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Adam Lee
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Edward P Gerstenfeld
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Henry H Hsia
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California, USA
| | | | - Bhupesh Pathik
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Saurabh Kumar
- Western Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; Department of Cardiology, Westmead Hospital and Westmead Applied Research Centre, Westmead, New South Wales, Australia
| | - Jonathan Kalman
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia; Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, Australia
| | - Geoffrey Lee
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia; Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, Australia.
| | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| |
Collapse
|
10
|
Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate. Med Biol Eng Comput 2022; 60:2463-2478. [PMID: 35867323 PMCID: PMC9365755 DOI: 10.1007/s11517-022-02621-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/07/2022] [Indexed: 11/02/2022]
Abstract
Characterizing patient-specific atrial conduction properties is important for understanding arrhythmia drivers, for predicting potential arrhythmia pathways, and for personalising treatment approaches. One metric that characterizes the health of the myocardial substrate is atrial conduction velocity, which describes the speed and direction of propagation of the electrical wavefront through the myocardium. Atrial conduction velocity mapping algorithms are under continuous development in research laboratories and in industry. In this review article, we give a broad overview of different categories of currently published methods for calculating CV, and give insight into their different advantages and disadvantages overall. We classify techniques into local, global, and inverse methods, and discuss these techniques with respect to their faithfulness to the biophysics, incorporation of uncertainty quantification, and their ability to take account of the atrial manifold.
Collapse
|
11
|
A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
Collapse
|
12
|
Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation. Int J Mol Sci 2022; 23:ijms23084216. [PMID: 35457044 PMCID: PMC9032062 DOI: 10.3390/ijms23084216] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 04/04/2022] [Accepted: 04/04/2022] [Indexed: 02/04/2023] Open
Abstract
The maintaining and initiating mechanisms of atrial fibrillation (AF) remain controversial. Deep learning is emerging as a powerful tool to better understand AF and improve its treatment, which remains suboptimal. This paper aims to provide a solution to automatically identify rotational activity drivers in endocardial electrograms (EGMs) with convolutional recurrent neural networks (CRNNs). The CRNN model was compared with two other state-of-the-art methods (SimpleCNN and attention-based time-incremental convolutional neural network (ATI-CNN)) for different input signals (unipolar EGMs, bipolar EGMs, and unipolar local activation times), sampling frequencies, and signal lengths. The proposed CRNN obtained a detection score based on the Matthews correlation coefficient of 0.680, an ATI-CNN score of 0.401, and a SimpleCNN score of 0.118, with bipolar EGMs as input signals exhibiting better overall performance. In terms of signal length and sampling frequency, no significant differences were found. The proposed architecture opens the way for new ablation strategies and driver detection methods to better understand the AF problem and its treatment.
Collapse
|
13
|
Lebert J, Ravi N, Fenton FH, Christoph J. Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks. Front Physiol 2022; 12:782176. [PMID: 34975536 PMCID: PMC8718715 DOI: 10.3389/fphys.2021.782176] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/11/2021] [Indexed: 11/15/2022] Open
Abstract
The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.
Collapse
Affiliation(s)
- Jan Lebert
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Namita Ravi
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, United States.,Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Flavio H Fenton
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Jan Christoph
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, United States
| |
Collapse
|
14
|
Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective. J Interv Card Electrophysiol 2022; 64:649-660. [PMID: 34981289 PMCID: PMC9470649 DOI: 10.1007/s10840-021-01101-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 12/01/2021] [Indexed: 12/17/2022]
Abstract
Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × 10−4 vs. 0.0032 ± 1 × 10−4, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × 10−5 vs. 0.0014 ± 4 × 10−5, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × 10−5 vs. 0.0019 ± 6 × 10−5, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × 10−5 vs. 0.0016 ± 4 × 10−5, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. Supplementary Information The online version contains supplementary material available at 10.1007/s10840-021-01101-z.
Collapse
|
15
|
Li QH, Van Nieuwenhuyse E, Xia YX, Pan JT, Duytschaever M, Knecht S, Vandersickel N, Zhou C, Panfilov AV, Zhang H. Finding type and location of the source of cardiac arrhythmias from the averaged flow velocity field using the determinant-trace method. Phys Rev E 2021; 104:064401. [PMID: 35030872 DOI: 10.1103/physreve.104.064401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 11/05/2021] [Indexed: 06/14/2023]
Abstract
Life threatening cardiac arrhythmias result from abnormal propagation of nonlinear electrical excitation waves in the heart. Finding the locations of the sources of these waves remains a challenging problem. This is mainly due to the low spatial resolution of electrode recordings of these waves. Also, these recordings are subjected to noise. In this paper, we develop a different approach: the AFV-DT method based on an averaged flow velocity (AFV) technique adopted from the analysis of optical flows and the determinant-trace (DT) method used for vector field analysis of dynamical systems. This method can find the location and determine all important types of sources found in excitable media such as focal activity, spiral waves, and waves rotating around obstacles. We test this method on in silico data of various wave excitation patterns obtained using the Luo-Rudy model for cardiac tissue. We show that the method works well for data with low spatial resolutions (up to 8×8) and is stable against noise. Finally, we apply it to two clinical cases and show that it can correctly identify the arrhythmia type and location. We discuss further steps on the development and improvement of this approach.
Collapse
Affiliation(s)
- Qi-Hao Li
- Department of Physics, Zhejiang University, Hangzhou 310027, China
| | | | - Yuan-Xun Xia
- Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Jun-Ting Pan
- Ocean College, Zhejiang University, Zhoushan 316021, China
| | | | | | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent 9000, Belgium
| | - Changsong Zhou
- Department of Physics, Zhejiang University, Hangzhou 310027, China
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Research Centre, HKBU Institute of Research and Continuing Education, Shenzhen 518057, China
- Beijing Computational Science Research Center, Beijing 100084, China
| | - Alexander V Panfilov
- Department of Physics and Astronomy, Ghent University, Ghent 9000, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg 620002, Russia
- World-Class Research Center "Digital biodesign and personalized healthcare," Sechenov University, Moscow 119146, Russia
| | - Hong Zhang
- Department of Physics, Zhejiang University, Hangzhou 310027, China
| |
Collapse
|
16
|
Vila M, Rocher S, Rivolta MW, Saiz J, Sassi R. Directed Network Mapping Approach to Rotor Localization in Atrial Fibrillation Simulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:730-733. [PMID: 34891395 DOI: 10.1109/embc46164.2021.9629911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Catheter ablation for atrial fibrillation (AF) is one of the most commonly performed electrophysiology procedures. Despite significant advances in our understanding of AF mechanisms in the last years, ablation outcomes remain suboptimal for many patients, particularly those with persistent or long-standing AF. A possible reason is that ablation techniques mainly focus on anatomic, rather than patient-specific functional targets for ablation. The identification of such ablation targets remains challenging. The purpose of this study is to investigate a novel approach based on directed networks, which allow the automatic detection of important arrhythmia mechanisms, that can be convenient for guiding the ablation strategy. The networks are generated by processing unipolar electrograms (EGMs) collected by the catheters positioned at the different regions of the atria. Network vertices represent the locations of the recordings and edges are determined using cross-covariance time-delay estimation method. The algorithm identifies rotational activity, spreading from vertex to vertex creating a cycle. This work is a simulation study and it uses a highly detailed computational 3D model of human atria in which sustained rotor activation of the atria was achieved. Virtual electrodes were placed on the endocardial surface, and EGMs were calculated at each of these electrodes. The propagation of the electric wave fronts in the atrial myocardium during AF is very complex, so in order to properly capture wave propagation patterns, we split EGMs into multiple short time frames. Then, a specific network for each of these time frames was generated, and the cycles repeating in consecutive networks point us to the stable rotor's location. The respective atrial voltage map served as reference. By detecting a cycle between the same 3 nodes in 19 out of 58 networks, where 10 of these networks were in consecutive time frames, a stable rotor was successfully located.
Collapse
|
17
|
Nezlobinsky T, Okenov A, Panfilov AV. Multiparametric analysis of geometric features of fibrotic textures leading to cardiac arrhythmias. Sci Rep 2021; 11:21111. [PMID: 34702936 PMCID: PMC8548304 DOI: 10.1038/s41598-021-00606-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/28/2021] [Indexed: 01/25/2023] Open
Abstract
One of the important questions in cardiac electrophysiology is to characterise the arrhythmogenic substrate; for example, from the texture of the cardiac fibrosis, which is considered one of the major arrhythmogenic conditions. In this paper, we perform an extensive in silico study of the relationships between various local geometric characteristics of fibrosis on the onset of cardiac arrhythmias. In order to define which texture characteristics have better predictive value, we induce arrhythmias by external stimulation, selecting 4363 textures in which arrhythmia can be induced and also selecting 4363 non-arrhythmogenic textures. For each texture, we determine such characteristics as cluster area, solidity, mean distance, local density and zig-zag propagation path, and compare them in arrhythmogenic and non-arrhythmogenic cases. Our study shows that geometrical characteristics, such as cluster area or solidity, turn out to be the most important for prediction of the arrhythmogenic textures. Overall, we were able to achieve an accuracy of 67% for the arrhythmogenic texture-classification problem. However, the accuracy of predictions depends on the size of the region chosen for the analysis. The optimal size for the local areas of the tissue was of the order of 0.28 of the wavelength of the arrhythmia. We discuss further developments and possible applications of this method for characterising the substrate of arrhythmias in fibrotic textures.
Collapse
Affiliation(s)
- T Nezlobinsky
- Department of Physics and Astronomy, Ghent University, Krijgslaan 281, 9000, Gent, Belgium.,Ural Federal University, Ekaterinburg, Russia
| | - A Okenov
- Department of Physics and Astronomy, Ghent University, Krijgslaan 281, 9000, Gent, Belgium
| | - A V Panfilov
- Department of Physics and Astronomy, Ghent University, Krijgslaan 281, 9000, Gent, Belgium. .,Ural Federal University, Ekaterinburg, Russia.
| |
Collapse
|
18
|
Vila M, Rivolta MW, Luongo G, Unger LA, Luik A, Gigli L, Lombardi F, Loewe A, Sassi R. Atrial Flutter Mechanism Detection Using Directed Network Mapping. Front Physiol 2021; 12:749635. [PMID: 34764882 PMCID: PMC8577834 DOI: 10.3389/fphys.2021.749635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.
Collapse
Affiliation(s)
- Muhamed Vila
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | | | - Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Anna Unger
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Lorenzo Gigli
- UOC Malattie Cardiovascolari, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Federico Lombardi
- UOC Malattie Cardiovascolari, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| |
Collapse
|
19
|
Wu Z, Liu Y, Tong L, Dong D, Deng D, Xia L. Current progress of computational modeling for guiding clinical atrial fibrillation ablation. J Zhejiang Univ Sci B 2021; 22:805-817. [PMID: 34636185 DOI: 10.1631/jzus.b2000727] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Atrial fibrillation (AF) is one of the most common arrhythmias, associated with high morbidity, mortality, and healthcare costs, and it places a significant burden on both individuals and society. Anti-arrhythmic drugs are the most commonly used strategy for treating AF. However, drug therapy faces challenges because of its limited efficacy and potential side effects. Catheter ablation is widely used as an alternative treatment for AF. Nevertheless, because the mechanism of AF is not fully understood, the recurrence rate after ablation remains high. In addition, the outcomes of ablation can vary significantly between medical institutions and patients, especially for persistent AF. Therefore, the issue of which ablation strategy is optimal is still far from settled. Computational modeling has the advantages of repeatable operation, low cost, freedom from risk, and complete control, and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance. This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF, from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation. Finally, we summarize current developments and challenges and provide our perspectives and suggestions for future directions.
Collapse
Affiliation(s)
- Zhenghong Wu
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Yunlong Liu
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Lv Tong
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Diandian Dong
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Ling Xia
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China.
| |
Collapse
|
20
|
Roney CH, Child N, Porter B, Sim I, Whitaker J, Clayton RH, Laughner JI, Shuros A, Neuzil P, Williams SE, Razavi RS, O'Neill M, Rinaldi CA, Taggart P, Wright M, Gill JS, Niederer SA. Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response. Front Physiol 2021; 12:707189. [PMID: 34646149 PMCID: PMC8503618 DOI: 10.3389/fphys.2021.707189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 08/24/2021] [Indexed: 11/13/2022] Open
Abstract
Electrical activation during atrial fibrillation (AF) appears chaotic and disorganised, which impedes characterisation of the underlying substrate and treatment planning. While globally chaotic, there may be local preferential activation pathways that represent potential ablation targets. This study aimed to identify preferential activation pathways during AF and predict the acute ablation response when these are targeted by pulmonary vein isolation (PVI). In patients with persistent AF (n = 14), simultaneous biatrial contact mapping with basket catheters was performed pre-ablation and following each ablation strategy (PVI, roof, and mitral lines). Unipolar wavefront activation directions were averaged over 10 s to identify preferential activation pathways. Clinical cases were classified as responders or non-responders to PVI during the procedure. Clinical data were augmented with a virtual cohort of 100 models. In AF pre-ablation, pathways originated from the pulmonary vein (PV) antra in PVI responders (7/7) but not in PVI non-responders (6/6). We proposed a novel index that measured activation waves from the PV antra into the atrial body. This index was significantly higher in PVI responders than non-responders (clinical: 16.3 vs. 3.7%, p = 0.04; simulated: 21.1 vs. 14.1%, p = 0.02). Overall, this novel technique and proof of concept study demonstrated that preferential activation pathways exist during AF. Targeting patient-specific activation pathways that flowed from the PV antra to the left atrial body using PVI resulted in AF termination during the procedure. These PV activation flow pathways may correspond to the presence of drivers in the PV regions.
Collapse
Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Nicholas Child
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Bradley Porter
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Richard H. Clayton
- INSIGNEO Institute for In Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | | | - Allan Shuros
- Boston Scientific Corp, St. Paul, MN, United States
| | - Petr Neuzil
- Department of Cardiology, Na Holmolce Hospital, Prague, Czechia
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza S. Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Peter Taggart
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Matt Wright
- Department of Cardiology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Jaswinder S. Gill
- Department of Cardiology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| |
Collapse
|
21
|
Van Nieuwenhuyse E, Martinez-Mateu L, Saiz J, Panfilov AV, Vandersickel N. Directed graph mapping exceeds phase mapping in discriminating true and false rotors detected with a basket catheter in a complex in-silico excitation pattern. Comput Biol Med 2021; 133:104381. [PMID: 33901713 PMCID: PMC8204274 DOI: 10.1016/j.compbiomed.2021.104381] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/02/2021] [Accepted: 04/02/2021] [Indexed: 12/04/2022]
Abstract
Atrial fibrillation (AF) is the most frequently encountered arrhythmia in clinical practise. One of the major problems in the management of AF is the difficulty in identifying the arrhythmia sources from clinical recordings. That difficulty occurs because it is currently impossible to verify algorithms which determine these sources in clinical data, as high resolution true excitation patterns cannot be recorded in patients. Therefore, alternative approaches, like computer modelling are of great interest. In a recent published study such an approach was applied for the verification of one of the most commonly used algorithms, phase mapping (PM). A meandering rotor was simulated in the right atrium and a basket catheter was placed at 3 different locations: at the Superior Vena Cava (SVC), the Crista Terminalis (CT) and at the Coronary Sinus (CS). It was shown that although PM can identify the true source, it also finds several false sources due to the far-field effects and interpolation errors in all three positions. In addition, the detection efficiency strongly depended on the basket location. Recently, a novel tool was developed to analyse any arrhythmia called Directed Graph Mapping (DGM). DGM is based on network theory and creates a directed graph of the excitation pattern, from which the location and the source of the arrhythmia can be detected. Therefore, the objective of the current study was to compare the efficiency of DGM with PM on the basket dataset of this meandering rotor. The DGM-tool was applied for a wide variety of conduction velocities (minimal and maximal), which are input parameters of DGM. Overall we found that DGM was able to distinguish between the true rotor and false rotors for both the SVC and CT basket positions. For example, for the SVC position with a CVmin=0.01cmms, DGM detected the true core with a prevalence of 82% versus 94% for PM. Three false rotors where detected for 39.16% (DGM) versus 100% (PM); 22.64% (DGM) versus 100% (PM); and 0% (DGM) versus 57% (PM). Increasing CVmin to 0.02cmms had a stronger effect on the false rotors than on the true rotor. This led to a detection rate of 56.6% for the true rotor, while all the other false rotors disappeared. A similar trend was observed for the CT position. For the CS position, DGM already had a low performance for the true rotor for CVmin=0.01cmms (14.7%). For CVmin=0.02cmms the false and the true rotors could therefore not be distinguished. We can conclude that DGM can overcome some of the limitations of PM by varying one of its input parameters (CVmin). The true rotor is less dependent on this parameter than the false rotors, which disappear at a CVmin=0.02cmms. In order to increase to detection rate of the true rotor, one can decrease CVmin and discard the new rotors which also appear at lower values of CVmin.
Collapse
Affiliation(s)
| | - Laura Martinez-Mateu
- Departamento de Teoría de La Señal y Las Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
| | - Alexander V Panfilov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium; Ural Federal University, Ekaterinburg, Russia; World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov University, Moscow, Russia
| | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| |
Collapse
|
22
|
Zhang D, Long X, Xu L, Werth J, Wijshoff R, Aarts RM, Andriessen P. Characterizing cardiorespiratory interaction in preterm infants across sleep states using visibility graph analysis. J Appl Physiol (1985) 2021; 130:1015-1024. [PMID: 33539263 DOI: 10.1152/japplphysiol.00333.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Cardiorespiratory interaction (CRI) has been intensively studied in adult sleep, yet not in preterm infants, in particular across different sleep states including wake (W), active sleep (AS), and quiet sleep (QS). The aim of this study was to quantify the interaction between cardiac and respiratory activities in different sleep states of preterm infants. The postmenstrual age (PMA) of preterm infants was also taken into consideration. The CRI during sleep was analyzed using a visibility graph (VG) method, enabling the nonlinear analysis of CRI in a complex network. For each sleep state, parameters quantifying various aspects of the CRI characteristics from constructed VG network including mean degree (Dm) and its variability (Dsd), clustering coefficient (CCm) and its variability (CCsd), assortativity coefficient (AC), and complexity (DSE) were extracted from the CRI networks. The interaction effect of sleep state and PMA was found to be statistically significant on all CRI parameters except for AC and DSE. The main effect between sleep state and CRI parameters was statistically significant except for CCm, and that between PMA and CRI parameters was statistically significant except for DSE. In conclusion, the CRI of preterm infants is associated with sleep states and PMA in general. For preterm infants with a larger PMA, CRI has a more clustered pattern during different sleep states, where QS shows a more regular, stratified, and stronger CRI than other states. In the future, these parameters can be potentially used to separate sleep states in preterm infants.NEW & NOTEWORTHY The interaction between cardiac and respiratory activities is investigated in preterm infant sleep using an advanced nonlinear method (visibility graph) and some important characteristics are shown to be significantly different across sleep states, which has not been studied before.
Collapse
Affiliation(s)
- Dandan Zhang
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Lin Xu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Jan Werth
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Ronald M Aarts
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Peter Andriessen
- Department of Neonatology, Máxima Medical Centre, Veldhoven, The Netherlands.,Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands
| |
Collapse
|
23
|
Evaluation of Directed Graph-Mapping in Complex Atrial Tachycardias. JACC Clin Electrophysiol 2021; 7:936-949. [PMID: 33812833 DOI: 10.1016/j.jacep.2020.12.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 11/23/2022]
Abstract
OBJECTIVES Directed graph-mapping (DGM) is a novel operator-independent automatic tool that can be applied to the identification of the atrial tachycardia (AT) mechanism. In the present study, for the first time, DGM was applied in complex AT cases, and diagnostic accuracy was evaluated. BACKGROUND Catheter ablation of ATs still represents a challenge, as the identification of the correct mechanism can be difficult. New algorithms for high-density activation mapping (HDAM) render an easier acquisition of more detailed maps; however, understanding of the mechanism and, thus, identification of the ablation targets, especially in complex cases, remains strongly operator-dependent. METHODS HDAMs acquired with the latest algorithm (COHERENT version 7, Biosense Webster, Irvine, California) were interpreted offline by 4 expert electrophysiologists, and the acquired electrode recordings with corresponding local activation times (LATs) were analyzed by DGM (also offline). Entrainment maneuvers (EM) were performed to understand the correct mechanism, which was then confirmed by successful ablation (13 cases were centrifugal, 10 cases were localized re-entry, 22 cases were macro-re-entry, and 6 were double-loops). In total, 51 ATs were retrospectively analyzed. We compared the diagnoses made by DGM were compared with those of the experts and with additional EM results. RESULTS In total, 51 ATs were retrospectively analyzed. Experts diagnosed the correct AT mechanism and location in 33 cases versus DGM in 38 cases. Diagnostic accuracy varied according to different AT mechanisms. The 13 centrifugal activation patterns were always correctly identified by both methods; 2 of 10 localized reentries were identified by the experts, whereas DGM diagnosed 7 of 10. For the macro-re-entries, 12 of 22 were correctly identified using HDAM versus 13 of 22 for DGM. Finally, 6 of 6 double-loops were correctly identified by the experts, versus 5 of 6 for DGM. CONCLUSIONS Even in complex cases, DGM provides an automatic, fast, and operator-independent tool to identify the AT mechanism and location and could be a valuable addition to current mapping technologies.
Collapse
|
24
|
Gagné S, Jacquemet V. Time resolution for wavefront and phase singularity tracking using activation maps in cardiac propagation models. CHAOS (WOODBURY, N.Y.) 2020; 30:033132. [PMID: 32237790 DOI: 10.1063/1.5133077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 03/02/2020] [Indexed: 06/11/2023]
Abstract
The dynamics of cardiac fibrillation can be described by the number, the trajectory, the stability, and the lifespan of phase singularities (PSs). Accurate PS tracking is straightforward in simple uniform tissues but becomes more challenging as fibrosis, structural heterogeneity, and strong anisotropy are combined. In this paper, we derive a mathematical formulation for PS tracking in two-dimensional reaction-diffusion models. The method simultaneously tracks wavefronts and PS based on activation maps at full spatiotemporal resolution. PS tracking is formulated as a linear assignment problem solved by the Hungarian algorithm. The cost matrix incorporates information about distances between PS, chirality, and wavefronts. A graph of PS trajectories is generated to represent the creations and annihilations of PS pairs. Structure-preserving graph transformations are applied to provide a simplified description at longer observation time scales. The approach is validated in 180 simulations of fibrillation in four different types of substrates featuring, respectively, wavebreaks, ionic heterogeneities, fibrosis, and breakthrough patterns. The time step of PS tracking is studied in the range from 0.1 to 10 ms. The results show the benefits of improving time resolution from 1 to 0.1 ms. The tracking error rate decreases by an order of magnitude because the occurrence of simultaneous events becomes less likely. As observed on PS survival curves, the graph-based analysis facilitates the identification of macroscopically stable rotors despite wavefront fragmentation by fibrosis.
Collapse
Affiliation(s)
- Samuel Gagné
- Institut de Génie Biomédical, Département de Pharmacologie et Physiologie, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Quebec H3C 3J7, Canada
| | - Vincent Jacquemet
- Institut de Génie Biomédical, Département de Pharmacologie et Physiologie, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Quebec H3C 3J7, Canada
| |
Collapse
|
25
|
Trayanova NA, Doshi AN, Prakosa A. How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1477. [PMID: 31917524 DOI: 10.1002/wsbm.1477] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 12/18/2022]
Abstract
Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient-specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient-specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof-of-concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine.
Collapse
Affiliation(s)
- Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ashish N Doshi
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
| |
Collapse
|